AI & ML
An Ordering Optimization for Fruit-Vegetable in High-Density Environment based on Genetic Algorithm
arXiv eess.SY0 views
arXiv:2611.07293v1 Announce Type: cross Abstract: This paper presents a robotic harvesting method that combines RGB-D fruit localization, GA-based harvest ordering, and waypoint-based robot motion in dense and unstructured crop environments. The global RGB-D camera provides 3D fruit positions for ordering, while the end-effector-mounted camera provides close-range observation during execution. The GA determines the harvesting sequence, and static or dynamic waypoints define the intermediate robot motion. The method was evaluated in a controlled laboratory testbed and in four-, five-, and six-fruit field conditions. GA solutions were compared with exhaustive enumeration, and both waypoint offsets were examined by parameter sensitivity analysis. Continuous execution reduced unnecessary Home-return motion, while the dynamic waypoint changed with the remaining fruit distribution without changing the GA sequence. In the field evaluation, the dynamic-waypoint continuous strategy produced 40 collision events and a 70.7% target-approach success rate, compared with 55 collision events and 65.3% for the static-waypoint continuous strategy.
Source:https://arxiv.org/abs/2611.07293PreviousHow do Transformers Perform Low-Level Control In-Context?NextA Synchronized Tri-Modality Wireless System for Multi-Point Non-Contact Cardiac Monitoring
Related posts
Ask HN: Can anyone run frontier AI models at usable speeds?
0Hacker News Ask
Looking for a CTO
0Hacker News Ask
Show HN: EasyRunCraft – An office and creative suite for your AI agent
0Hacker News Show
Show HN: Current – Change control for AI agents (Elixir)
0Hacker News Show
Show HN: Infinity – Git-style branching for AI chats
0Hacker News Show
Show HN: A tool for looping and transcribing YouTube Videos
0Hacker News Show